100% Free Snowflake ARA-R01 Practice Exam Questions Online 2026

Question 1

What is a key consideration when setting up search optimization service for a table?

A : Search optimization service works best with a column that has a minimum of 100 K distinct values.

B : Search optimization service can significantly improve query performance on partitioned external tables.

C : Search optimization service can help to optimize storage usage by compressing the data into a GZIP format.

D : The table must be clustered with a key having multiple columns for effective search optimization.

Answer: A

Question 2

A retail company has over 3000 stores all using the same Point of Sale (POS) system. The company wants todeliver near real-time sales results to category managers. The stores operate in a variety of time zones andexhibit a dynamic range of transactions each minute, with some stores having higher sales volumes thanothers.Sales results are provided in a uniform fashion using data engineered fields that will be calculated in acomplex data pipeline. Calculations include exceptions, aggregations, and scoring using external functionsinterfaced to scoring algorithms. The source data for aggregations has over 100M rows.Every minute, the POS sends all sales transactions files to a cloud storage location with a naming conventionthat includes store numbers and timestamps to identify the set of transactions contained in the files. The filesare typically less than 10MB in size.How can the near real-time results be provided to the category managers? (Select TWO).

A : All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion.

B : A Snowpipe should be created and configured with AUTO_INGEST = true. A stream should be created to process INSERTS into a single target table using the stream metadata to inform the store number and timestamps.

C : A stream should be created to accumulate the near real-time data and a task should be created that runs at a frequency that matches the real-time analytics needs.

D : An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs

E : The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement.

Answer: B,C

Question 3

A user is executing the following command sequentially within a timeframe of 10 minutes from start to finish:What would be the output of this query?

A : Table T_SALES_CLONE successfully created.

B : Time Travel data is not available for table T_SALES.

C : The offset -> is not a valid clause in the clone operation.

D : Syntax error line 1 at position 58 unexpected 'at’.

Answer: A

Question 4

The Data Engineering team at a large manufacturing company needs to engineer data coming from manysources to support a wide variety of use cases and data consumer requirements which include:1) Finance and Vendor Management team members who require reporting and visualization2) Data Science team members who require access to raw data for ML model development3) Sales team members who require engineered and protected data for data monetizationWhat Snowflake data modeling approaches will meet these requirements? (Choose two.)

A : Consolidate data in the company’s data lake and use EXTERNAL TABLES.

B : Create a raw database for landing and persisting raw data entering the data pipelines.

C : Create a set of profile-specific databases that aligns data with usage patterns.

D : Create a single star schema in a single database to support all consumers’ requirements.

E : Create a Data Vault as the sole data pipeline endpoint and have all consumers directly access the Vault.

Answer: B,C

Question 5

A company is designing a process for importing a large amount of loT JSON data from cloud storage into Snowflake. New sets of loT data get generated and uploaded approximately every 5 minutes. Once the loT data is in Snowflake, the company needs up-to-date information from an external vendor to join to the data. This data is then presented to users through a dashboard that shows different levels of aggregation. The external vendor is a Snowflake customer. What solution will MINIMIZE complexity and MAXIMIZE performance?

A : 1. Create an external table over the JSON data in cloud storage. 2. Create a task that runs every 5 minutes to run a transformation procedure on new data, based on a saved timestamp. 3. Ask the vendor to expose an API so an external function can be used to generate a call to join the data back to the loT data in the transformation procedure. 4. Give the transformed table access to the dashboard tool. 5. Perform the aggregations on the dashboard tool.

B : 1. Create an external table over the JSON data in cloud storage. 2. Create a task that runs every 5 minutes to run a transformation procedure on new data based on a saved timestamp. 3. Ask the vendor to create a data share with the required data that can be imported into the company's Snowflake account. 4. Join the vendor-s data back to the loT data using a transformation procedure. 5. Create views over the larger dataset to perform the aggregations required by the dashboard. 6. Give the views access to the dashboard tool.

C : privileges to create or manage shares), they can add an Enterprise edition account as a consumer. This feature enables flexibility in data sharing across different Snowflake account editions, facilitating broader data collaboration and accessibility.References: Snowflake-s data sharing documentation and the specifics of edition-based capabilities discussed in SnowPro Advanced: Architect certification materials. Question #:47 A company is designing a process for importing a large amount of loT JSON data from cloud storage into Snowflake. New sets of loT data get generated and uploaded approximately every 5 minutes. Once the loT data is in Snowflake, the company needs up-to-date information from an external vendor to join to the data. This data is then presented to users through a dashboard that shows different levels of aggregation. The external vendor is a Snowflake customer. What solution will MINIMIZE complexity and MAXIMIZE performance? 1. Create an external table over the JSON data in cloud storage. 2. Create a task that runs every 5 minutes to run a transformation procedure on new data, based on a saved timestamp. 3. Ask the vendor to expose an API so an external function can be used to generate a call to join the data back to the loT data in the transformation procedure. 4. Give the transformed table access to the dashboard tool. 5. Perform the aggregations on the dashboard tool. 1. Create an external table over the JSON data in cloud storage. 2. Create a task that runs every 5 minutes to run a transformation procedure on new data based on a saved timestamp. 3. Ask the vendor to create a data share with the required data that can be imported into the company's Snowflake account. 4. Join the vendor-s data back to the loT data using a transformation procedure. 5. Create views over the larger dataset to perform the aggregations required by the dashboard. 6. Give the views access to the dashboard tool. 1. Create a Snowpipe to bring the JSON data into Snowflake. 2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives. 3. Ask the vendor to expose an API so an external function call can be made to join the vendor-s data back to the loT data in a transformation procedure. 4. Create materialized views over the larger dataset to perform the aggregations required by the dashboard. 5. Give the materialized views access to the dashboard tool

D : 1. Create a Snowpipe to bring the JSON data into Snowflake. 2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives. 3. Ask the vendor to create a data share with the required data that is then imported into the Snowflake account. 4. Join the vendor-s data back to the loT data in a transformation procedure 5. Create materialized views over the larger dataset to perform the aggregations required by the dashboard. 6. Give the materialized views access to the dashboard tool.

Answer: D

Free Snowflake ARA-R01 Practice Exam Questions and Answers 2026